Not that outrageous.
https://www.cnn.com/2020/05/11/us/california-inmates-coronav...
According to this Sep 2020 article on reason.com (1) they quote CDC numbers of a 99.98 survival rate (0.02% IFR) for 20-to-49-year-olds. Or a 99.997% survival rate (0.003 IFR) percent among people 19 or less.
1: https://www.google.com/amp/s/reason.com/2020/09/29/the-lates...
What happens when you decide it's good enough and release them, and the residual infection sweeps through that population like wildfire?
As opposed to 100% of the population? It sounds like an improvement to me. I'm suggesting relaxing restrictions for a part of the population, not increasing them.
I'm 53. I have asthma and I really hate hospitals. Right now, I have been going for groceries about once a week. I've gone to some appointments, but I've cancelled others. I've been getting take-out some, and I even went on one shopping expedition for craft supplies. I'm relatively comfortable with that because, while I have to assume everyone else is potentially infected, I can also assume that most of them are taking steps to protect me---a mask is significantly more effective at preventing spread from someone infected than it is at preventing an infection of the wearer.
Close quarantine means no going to the store at all. Not going for walks. It specifically means no human contact outside the people you are quarantined with.
If you relax "restrictions" on a part of the population, more of that population becomes infected. If you do not increase the restrictions on the remainder of the population, the higher prevalence increases the transmission rate in that remainder. And thus deaths.
I'm sorry if my existence is inconveniencing you.
This might not be a terrible idea, though, if compared to a several-year-extension of what we have now... because over time, the cumulative probably of exposure for the vulnerable will just keep rising and rising if we stay at something like the status quo.
But... that's where things like vaccine and treatment development come in. If a vaccine makes catching it much less likely in 6 months, or treatment improvements make it much less deadly even for the vulnerable in six months, then it's worth spending another 6 months in the current situation.
Here are the authors. They are well credentialed
Dr. Martin Kulldorff, professor of medicine at Harvard University, a biostatistician, and epidemiologist with expertise in detecting and monitoring of infectious disease outbreaks and vaccine safety evaluations.
Dr. Sunetra Gupta, professor at Oxford University, an epidemiologist with expertise in immunology, vaccine development, and mathematical modeling of infectious diseases.
Dr. Jay Bhattacharya, professor at Stanford University Medical School, a physician, epidemiologist, health economist, and public health policy expert focusing on infectious diseases and vulnerable populations.
One of the authors' twitter thread with the paper's summary: https://twitter.com/WesPegden/status/1288140129677332482
There are many problems with the GBD, but the simplest is that we don't know who the high-risk groups are. Yes, we know age and certain categories of pre-existing condition make for higher risk of death. But we also know that perfectly healthy young people end up with strokes, heart damage, and lung damage, and we're not really sure why. We don't know why some people end up with debilitating symptoms months after infection.
We don't even know if herd immunity is actually possible, or if we'd be committing ourselves to years of intermittent lockdown controls as local outbreaks come and go.
This paper is a similar (if slightly more mathematically detailed) approach, and is more recent: https://www.pnas.org/content/early/2020/09/21/2008087117. It comes to the opposite conclusion. What they find is that while it's technically possible to achieve herd immunity this way, it's logistically unfeasible. It needs monitoring, compliance, and reactiveness that we demonstrably can't (or won't) implement - if we could, we wouldn't be in this mess.
Besides which, neither this paper nor that supports any idea that these three are "leading experts". As far as I can see they're vocal and have a history of being proved wrong by events.
We absolutely do. We have such a wealth of data and the signal is very strong.
> That paper doesn't consider reinfection risk or non-fatal outcomes.
That's because reinfection is extremely rare and risk for non-fatal outcomes is typical of other influenza like illnesses. An interesting note is that many / most people have some sort of cross-protection through T-cell immunity (likely from other coronaviruses).
> We don't even know if herd immunity is actually possible
Yes we do. Pretty much every disease tails off. The only debate right now is where this threshold is at for various jurisdictions. It is likely as low as 20%. The 60% number quoted early in the pandemic was assuming homogenous population with equal susceptibility and perfect mixing.
> This paper is a similar (if slightly more mathematically detailed) approach, and is more recent: https://www.pnas.org/content/early/2020/09/21/2008087117. It comes to the opposite conclusion. What they find is that while it's technically possible to achieve herd immunity this way, it's logistically unfeasible.
All models are wrong but some are useful. If this model cannot explain real data from cities and countries (eg: stockholm, UK locales) then it is relatively useless.
We know who is likely to die. We do not know who is at risk of a life-long debilitating illness.
> That's because reinfection is extremely rare
We don't know this. What we know is that reinfection with a different strain is rarely detected, and that's a long way from the same thing.
> risk for non-fatal outcomes is typical of other influenza like illnesses
This is false.
> An interesting note is that many / most people have some sort of cross-protection through T-cell immunity (likely from other coronaviruses).
At best this is optimistic. We know some (less than half) have a T-cell response. We don't know yet if that response is beneficial, harmful, or has no effect at all. It would be premature to start any sort of public health intervention founded on this assumption.
> Yes we do. Pretty much every disease tails off.
This strongly depends on the reinfection rate. Which we don't know.
> The only debate right now is where this threshold is at for various jurisdictions. It is likely as low as 20%.
This is false. To get anywhere near 20% you need to know the effect of the T-cell response, or have some other mechanism for discounting a large portion of the population.
> All models are wrong but some are useful. If this model cannot explain real data from cities and countries (eg: stockholm, UK locales) then it is relatively useless.
Have you read either of them? Both models in this thread are predictive models of situations that haven't happened yet. Both use real data (from the US and the UK). Neither can describe reality, so do we throw them both out? That leaves the GBD lot with no epidemiological support at all, which would make my point rather concisely.
We simply don't have enough information to know whether the GBD proposal is safe or, even if it was, whether it could be implemented, and it's all the more suspicious because its three proponents have been making very similar arguments against general lockdown since at least April, when we knew dramatically less. They do not seem to have changed their stances based on new information, which moves the GBD out of science and into politics. Only they're leaning on their academic credentials to lend it airs of legitimacy it can't back up, which makes it complete, utter bullshit that nobody should pay any attention to. It's preying on desperation and optimism to deepen social division and reinforce political hysteria at the worst possible time. No credible health authority is paying any attention to it, nor should they. Please don't bring that sort of content to HN.
https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/comm...
(fyi: they bury the lede on this page: you have to click through to their horribly slow interactive viewer widget to see the multiples.)
Earlier this week we took my son in because:
1. Our neighbors have recently recovered from Coronavirus
2. Our son had a cold / fever around the same time lasting several days
3. He had several days of diarrhea after the cold symptoms subsided
I talked to my coworker whose wife is a doctor and he relayed the symptoms to her and she said it very well could be a mild coronavirus infection.
We weren't concerned for his health because he's handled it just fine, but we figured it would be good to know in terms of avoiding spreading it to other people.
So we called and set up an appointment. They got us in like an hour and half after we called.
Got there and the doctor said, "Nope, no need for a test."
At this point I'm extremely skeptical that the number counts are in any way accurate. Also I've lost faith in the "we're not doing enough testing" argument since we went and tried to get tested and they turned us away.
That does not imply that there is enough testing. That just implies that your doctor or health care system is unwilling to test someone in your circumstances. "Not doing enough testing" is not referring to just test kids not being available, it is also referring to health system policies and willingness/unwilingness to test people.
We attempted to get tested and they said no, not worth the bother.
That seems contradictory to me.
Japan did very little testing and has a relatively low death rate so far.
If you're in an area where neither of those things apply then, for a child a test isn't going to change anything. It's not going to change their treatment. If it's positive they'll tell you to isolate, but if it's negative, there's a high enough false negative rate, and there are enough cases that it's still very likely they have it, so you'll still want to treat it like they do.
Not necessarily, it could mean that the people that need to get tested aren't all getting tested.
The reason is not that some people are not willing to get test. The reason is also capacity, aldo policies on who is allowed to get test etc. Your case is literally someone who should be tested and was not, hence "not enough testing".
Here, I can get comertial test if I am willing to pay. No one will send me home. And if I was in contact with covid infected person, I get test for free (it is mandatory). And if I live in household with sick person, I have mandatory quarantine.
Sounds like where you live is no contract tracing and thus not enough testing.
It can also be the case that insufficient testing is available in your area.
I know a group of people that were tested because of potential exposure (they were tested based on 1 person reporting symptoms; the additional tests were done prior to that result coming back).
If you're not sick enough to require hospitalization, and especially if you're un-insured and poor and don't want to pay a couple-hundred bucks out of pocket, then you'll probably just ride it out and never get tested. I'm sure this is the state of things for tens of millions of Americans.
That's because the test won't change anything. If it's positive, they'll tell you to isolate your kid. If it's negative, there's a high enough false negative rate that you'll still need to basically treat them like they have COVID (stay home and isolate).
>And isolation of sick patient from viral reservoir (such as fomites and other asymptomatic sick) is known to be important too, as it changes the viral inoculum dose.
This isn't known to be important. If a person has been infected long enough that they are already symptomatic then "viral inoculum dose" is irrelevant.
CONFIRMED cases. The total number of cases is probably 5-10x that.
I live in the Deep South, and honestly I suspect that our curves have fallen simply due to a "limited" herd immunity effect (i.e. the groups of people most likely to catch COVID have already done so in large enough numbers). I certainly haven't observed any significant change in behaviors since the July peak, yet the numbers are falling like a rock regardless.
If the IFR is around 1% we would expect around 1,000,000 deaths if one third of Americans have to be infected for herd immunity. So that would suggest the US is 20 percent of the way there.
Given the haphazard way of calculating these numbers I would, however, put huge error bars around some (something like ±15 percentage points at least).
I too suspect areas are achieving some limited herd immunity. I don't think the behavioral changes adopted in the US have done much. Mostly, I just think less social people aren't getting it. More social people are.
https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scena...
And the opposite is now happening in the north (again). People come inside as the weather gets colder, and respiratory infections in general get far worse.
Picking FL as an example: deaths are down only about 50% since the peak in August (7-day averaged), and the numbers are surprisingly "sticky" (in the sense that they're not going down all that quickly.) For the record, FL lost 139 people yesterday; that's nearly the capacity of a 737.
139 deaths were reported; those deaths actually occurred over the last several months. Which means we won't know how many actually died yesterday for a while, but it's likely well below 139. Jennifer Cabrera posts regular updates with dates of deaths, e.g. https://twitter.com/jhaskinscabrera/status/13139124858340884...
https://public.tableau.com/profile/peter.james.walker#!/vizh...
Sorry, I didn't mean to convey that impression. Florida has been regularly reporting daily death counts that include deaths from several months ago.
I would need to know that the older, higher numbers were not also subject to the same delays.
They were, in the other direction. If you look at the date-of-death chart in the thread I linked, you'll see that for a few weeks there were consistently over 200 actual deaths per day, while the reported 7-day average in your chart never reached 200. The delay means that the reported count will be lower than the actual count when deaths are rising, and higher than the actual count when deaths are falling.
And of course we can't be sure which of those categories we're in at any particular point in time; if deaths do start to increase again, it may not be noticeable in the reported numbers for several days. But based on the hospitalization trend I believe it's probable that the current reported numbers overstate the actual deaths.
You'd like to know if people are wearing masks at church or if family get-togethers are now outdoors.
However, I'm looking at my local health department's ZIP-code-by-ZIP-code infection map of the metro area. And it seems almost entirely correlated with poverty, not privilege. Infection spread seems mostly due to "essential" workers continuing to work. That's a problematic thing to point out, because I don't think we can or will solve for that. But it's plain as day.
I get the idea of flatten the curve but states that already got hit hard don't really have any reason to impose additional restrictions they wouldn't do anything.
It's pretty obvious to people watching that the impression was created by liberal media that just wants to bash conservatives.
A cursory check of the numbers shows that it has no basis in reality.
Top 5 states:
Louisiana, Mississippi, Florida, North Dakota, Alabama
https://www.nytimes.com/interactive/2020/05/05/us/coronaviru...
In any event TX, NJ, and FL all have similar reported COVID deaths, but CA has fewer excess deaths than TX or NJ. Some of that is probably due to e.g. reduced automotive mortalities under more stringent lockdown, but I don't think the data you posted indicats that CA is e.g. worse than the other 3 states at reporting COVID deaths (otherwise we would see a much higher excess death count in CA compared to those 3 states).
That said, at my regional college in the Deep South, case numbers have dropped noticeably since the beginning of term - instead of 30 cases every day we have now ten, and there have been remarkably few outbreaks at frathouses, all that despite no organized testing.
So you'd really need to look at per capita infection rates and control for density, not absolute numbers.
For some anecdata, in my moderate county we have a mask mandate. The surrounding less populated and much more conservative areas don't. Since the mask mandate was put in place more than half of the cases in my county's hospitals have been from the surrounding counties. Despite that fact that they have an order of magnitude fewer people, and despite that fact that we have far more people living in poverty in absolute terms than they do.
https://covid19-projections.com/ (Excellent source during these times. Sad to see them deciding to stop moving forward but it's good for now).
See, e.g., https://www.nature.com/articles/s41591-020-1083-1
This is why I watch florida like an eagle, because if it stops going up there we know we got a very good estimate to know when the top is.
~2.5-3% (730k/21.5m) of Florida has had it and it's slowed dramatically (use to be like 16k/day now down to 3). It feels reasonable that herd immunity starts slowing down the virus pretty fast somewhere around 25-30%. It seems reasonable it may come to a complete halt by 47%.
But if the second wave data for the UK is anything to go by, confirmed cases vs actual cases was at _least_ 5x for the first wave.
There obivously are other factors, but with increased testing that multiplier only climbs, bringing herd immunity numbers actually within reasonable grasp.
I strongly suspect there have been similar effects in the US.
Is this what they call napkin math in the UK? Back of the cigarette pack math?
It's just the name of that type of maths because people used to use whatever small, available, piece of paper was around - so cigarette packs some time ago (you definitely could write on them with a ballpoint pen (ie un bic).
Getting tangential, napkins to me in the UK have always been cloth, and we have serviettes (a French word, meaning sheet IIRC) made of paper to wipe our mouths with.
Our language gets more and more influenced by USA "English" usage, so perhaps youth would just call it a napkin. People do say 'paper napkin' but without the qualifier it's a fancy piece of cloth [to me].
Our language gets more and more influenced by USA "English" usage,
I can't remember exactly where I heard it, but I believe robust herd immunity in human populations has never been achieved for a virus like this without the widespread use of a vaccine. Which makes sense, because there's evolutionary pressure on viruses to adapt, and so many diseases remained endemic and common until vaccines where introduced for them.
Edit: in response to the dead reply: the Spanish Flu didn't disappear. It killed tens of millions (out of a much smaller world population) and persisted for decades as a seasonal flu. IIRC, it didn't get eclipsed by other strains until the 50s.
EDIT: seriously, downvoting this comment? Can't imagine why and would like to know.
With natural infection, the socially active will be infected and removed first, which lowers the fraction needed.
It's not always effective, because they have to predict when they make it what strains of flu will be prevalent in the next flu season, and they don't always get that right, but in that case you are no worse off than if you had not gotten the vaccine. But when they do get it right, it can save you from getting the flu. That's certainly worth a few minutes getting it an a couple days with a sore arm.
With a COVID vaccine, I doubt it will get anywhere near the same fraction of takers as the flu vaccine.
1. The anti-vaccine crowd probably won't take it.
2. The "COVID is no worse than a mild flu" crowd probably won't take it. Even if they do, it won't be at a higher rate than they take the flu vaccine.
3. The "COVID is a hoax" crowd probably won't take it.
4. A lot of the people who believe COVID is real and serious will probably be turned off if the approval seems to have involved politicians forcing approval over the objection of scientists who say it is not ready yet. (Especially if those politicians have also been pushing the "no worse than a mild flu" narrative).
It will if closures are lifted but vaccination is made a requirement (either privately or by government mandate) for on-site work, schooling, etc. The flu vaccine is treated as primarily a personal preventive medical treatment; while it is encouraged for public as well as personal health reasons, there is no force put behind it. But it is quite possible, and there is plenty of precedent for, public health measures to be mandated (especially a conditional mandate, like for schools, work in particular conditions--including any public content or even on-premises work) and enforced.
This is a false binary state space.
The implication here is that having "tested positive" is equivalent to having an "infection". An infection, by definition, is an alteration of the biological state of the entity with observable side-effects (symptoms). It is patently false to assert that "testing positive" for this virus is 100% indicative of an "infection".
Further, "100M infections/recovered" implies that this infection can lead to "recovered" implying that the other alternative to recovere is a terminal/chronic condition. I guess this makes sense if we agree that many millions of "infected" immediately "recover" after testing positive, given that a substantial subset of those who "test positive" are "asymptomatic", reasonably understood as not-ill, not-sick, not-infected. Thus insta "recovery".
My overall point here is that the permitted vocabulary of speaking and reasoning about this phenomena is inexplicably illiterate. Whether this permitted simplistic vocabulary of discourse is by design or a symptomatic of the state of humanity, the inevitable consequence is a degradation of analysis and sub-optimal solutions.
At the current rate of +50K infections per day, that's 20 days per 1M infections, so we need 20 days * 92 = 5 years before we achieve herd immunity (best case, assuming no vaccines)? That doesn't seem right.